Load scripts: loads libraries and useful scripts used in the analyses; all .R files contained in scripts at the root of the factory are automatically loaded
Load data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)These scripts will load:
.R files inside /scripts/.R files inside /src/These scripts also contain routines to access the latest clean encrypted data (see next section).
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 392,584 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 392,574 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 14
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 7
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 1
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 8
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 5
## 116 2020-06-24 East of England 4
## 117 2020-06-25 East of England 1
## 118 2020-06-26 East of England 5
## 119 2020-06-27 East of England 6
## 120 2020-06-28 East of England 8
## 121 2020-06-29 East of England 4
## 122 2020-06-30 East of England 5
## 123 2020-07-01 East of England 2
## 124 2020-07-02 East of England 5
## 125 2020-07-03 East of England 0
## 126 2020-07-04 East of England 3
## 127 2020-07-05 East of England 1
## 128 2020-07-06 East of England 2
## 129 2020-07-07 East of England 2
## 130 2020-07-08 East of England 0
## 131 2020-07-09 East of England 8
## 132 2020-07-10 East of England 4
## 133 2020-07-11 East of England 2
## 134 2020-07-12 East of England 1
## 135 2020-07-13 East of England 8
## 136 2020-07-14 East of England 2
## 137 2020-07-15 East of England 0
## 138 2020-07-16 East of England 0
## 139 2020-07-17 East of England 0
## 140 2020-07-18 East of England 0
## 141 2020-07-19 East of England 1
## 142 2020-07-20 East of England 1
## 143 2020-07-21 East of England 1
## 144 2020-07-22 East of England 2
## 145 2020-07-23 East of England 1
## 146 2020-07-24 East of England 1
## 147 2020-07-25 East of England 0
## 148 2020-07-26 East of England 1
## 149 2020-07-27 East of England 1
## 150 2020-07-28 East of England 2
## 151 2020-07-29 East of England 0
## 152 2020-07-30 East of England 0
## 153 2020-07-31 East of England 1
## 154 2020-08-01 East of England 0
## 155 2020-08-02 East of England 0
## 156 2020-08-03 East of England 0
## 157 2020-08-04 East of England 1
## 158 2020-08-05 East of England 1
## 159 2020-08-06 East of England 0
## 160 2020-08-07 East of England 1
## 161 2020-08-08 East of England 0
## 162 2020-08-09 East of England 0
## 163 2020-08-10 East of England 1
## 164 2020-08-11 East of England 2
## 165 2020-08-12 East of England 1
## 166 2020-08-13 East of England 0
## 167 2020-08-14 East of England 1
## 168 2020-08-15 East of England 1
## 169 2020-08-16 East of England 0
## 170 2020-08-17 East of England 0
## 171 2020-08-18 East of England 2
## 172 2020-08-19 East of England 1
## 173 2020-08-20 East of England 1
## 174 2020-08-21 East of England 0
## 175 2020-08-22 East of England 1
## 176 2020-08-23 East of England 1
## 177 2020-08-24 East of England 0
## 178 2020-08-25 East of England 0
## 179 2020-08-26 East of England 1
## 180 2020-08-27 East of England 1
## 181 2020-08-28 East of England 0
## 182 2020-08-29 East of England 0
## 183 2020-08-30 East of England 0
## 184 2020-08-31 East of England 0
## 185 2020-09-01 East of England 0
## 186 2020-09-02 East of England 0
## 187 2020-09-03 East of England 1
## 188 2020-09-04 East of England 1
## 189 2020-09-05 East of England 0
## 190 2020-09-06 East of England 1
## 191 2020-09-07 East of England 0
## 192 2020-09-08 East of England 0
## 193 2020-09-09 East of England 0
## 194 2020-09-10 East of England 0
## 195 2020-09-11 East of England 0
## 196 2020-09-12 East of England 0
## 197 2020-09-13 East of England 1
## 198 2020-09-14 East of England 1
## 199 2020-09-15 East of England 0
## 200 2020-09-16 East of England 0
## 201 2020-09-17 East of England 0
## 202 2020-09-18 East of England 0
## 203 2020-09-19 East of England 0
## 204 2020-09-20 East of England 2
## 205 2020-09-21 East of England 0
## 206 2020-09-22 East of England 2
## 207 2020-09-23 East of England 1
## 208 2020-09-24 East of England 0
## 209 2020-09-25 East of England 1
## 210 2020-09-26 East of England 1
## 211 2020-09-27 East of England 1
## 212 2020-09-28 East of England 2
## 213 2020-09-29 East of England 2
## 214 2020-09-30 East of England 2
## 215 2020-10-01 East of England 2
## 216 2020-10-02 East of England 1
## 217 2020-10-03 East of England 1
## 218 2020-10-04 East of England 0
## 219 2020-10-05 East of England 0
## 220 2020-10-06 East of England 4
## 221 2020-10-07 East of England 6
## 222 2020-10-08 East of England 3
## 223 2020-10-09 East of England 1
## 224 2020-10-10 East of England 6
## 225 2020-10-11 East of England 2
## 226 2020-10-12 East of England 2
## 227 2020-10-13 East of England 1
## 228 2020-10-14 East of England 3
## 229 2020-10-15 East of England 4
## 230 2020-10-16 East of England 5
## 231 2020-10-17 East of England 6
## 232 2020-10-18 East of England 7
## 233 2020-10-19 East of England 5
## 234 2020-10-20 East of England 9
## 235 2020-10-21 East of England 7
## 236 2020-10-22 East of England 7
## 237 2020-10-23 East of England 14
## 238 2020-10-24 East of England 1
## 239 2020-10-25 East of England 10
## 240 2020-10-26 East of England 10
## 241 2020-10-27 East of England 8
## 242 2020-10-28 East of England 12
## 243 2020-10-29 East of England 10
## 244 2020-10-30 East of England 12
## 245 2020-10-31 East of England 15
## 246 2020-11-01 East of England 14
## 247 2020-11-02 East of England 9
## 248 2020-11-03 East of England 14
## 249 2020-11-04 East of England 11
## 250 2020-11-05 East of England 11
## 251 2020-11-06 East of England 18
## 252 2020-11-07 East of England 10
## 253 2020-11-08 East of England 13
## 254 2020-11-09 East of England 16
## 255 2020-11-10 East of England 26
## 256 2020-11-11 East of England 14
## 257 2020-11-12 East of England 14
## 258 2020-11-13 East of England 21
## 259 2020-11-14 East of England 19
## 260 2020-11-15 East of England 13
## 261 2020-11-16 East of England 11
## 262 2020-11-17 East of England 17
## 263 2020-11-18 East of England 19
## 264 2020-11-19 East of England 23
## 265 2020-11-20 East of England 24
## 266 2020-11-21 East of England 19
## 267 2020-11-22 East of England 21
## 268 2020-11-23 East of England 18
## 269 2020-11-24 East of England 21
## 270 2020-11-25 East of England 19
## 271 2020-11-26 East of England 19
## 272 2020-11-27 East of England 14
## 273 2020-11-28 East of England 28
## 274 2020-11-29 East of England 19
## 275 2020-11-30 East of England 22
## 276 2020-12-01 East of England 24
## 277 2020-12-02 East of England 18
## 278 2020-12-03 East of England 23
## 279 2020-12-04 East of England 24
## 280 2020-12-05 East of England 22
## 281 2020-12-06 East of England 19
## 282 2020-12-07 East of England 16
## 283 2020-12-08 East of England 24
## 284 2020-12-09 East of England 19
## 285 2020-12-10 East of England 31
## 286 2020-12-11 East of England 30
## 287 2020-12-12 East of England 25
## 288 2020-12-13 East of England 23
## 289 2020-12-14 East of England 27
## 290 2020-12-15 East of England 32
## 291 2020-12-16 East of England 29
## 292 2020-12-17 East of England 37
## 293 2020-12-18 East of England 36
## 294 2020-12-19 East of England 42
## 295 2020-12-20 East of England 45
## 296 2020-12-21 East of England 55
## 297 2020-12-22 East of England 45
## 298 2020-12-23 East of England 52
## 299 2020-12-24 East of England 43
## 300 2020-12-25 East of England 51
## 301 2020-12-26 East of England 55
## 302 2020-12-27 East of England 43
## 303 2020-12-28 East of England 55
## 304 2020-12-29 East of England 43
## 305 2020-12-30 East of England 57
## 306 2020-12-31 East of England 54
## 307 2021-01-01 East of England 66
## 308 2021-01-02 East of England 38
## 309 2021-01-03 East of England 52
## 310 2021-01-04 East of England 42
## 311 2021-01-05 East of England 17
## 312 2020-03-01 London 0
## 313 2020-03-02 London 0
## 314 2020-03-03 London 0
## 315 2020-03-04 London 0
## 316 2020-03-05 London 0
## 317 2020-03-06 London 1
## 318 2020-03-07 London 0
## 319 2020-03-08 London 0
## 320 2020-03-09 London 1
## 321 2020-03-10 London 0
## 322 2020-03-11 London 5
## 323 2020-03-12 London 6
## 324 2020-03-13 London 10
## 325 2020-03-14 London 13
## 326 2020-03-15 London 9
## 327 2020-03-16 London 15
## 328 2020-03-17 London 23
## 329 2020-03-18 London 28
## 330 2020-03-19 London 25
## 331 2020-03-20 London 44
## 332 2020-03-21 London 49
## 333 2020-03-22 London 54
## 334 2020-03-23 London 63
## 335 2020-03-24 London 86
## 336 2020-03-25 London 112
## 337 2020-03-26 London 130
## 338 2020-03-27 London 130
## 339 2020-03-28 London 123
## 340 2020-03-29 London 145
## 341 2020-03-30 London 151
## 342 2020-03-31 London 183
## 343 2020-04-01 London 202
## 344 2020-04-02 London 191
## 345 2020-04-03 London 199
## 346 2020-04-04 London 231
## 347 2020-04-05 London 195
## 348 2020-04-06 London 198
## 349 2020-04-07 London 220
## 350 2020-04-08 London 239
## 351 2020-04-09 London 207
## 352 2020-04-10 London 171
## 353 2020-04-11 London 178
## 354 2020-04-12 London 159
## 355 2020-04-13 London 166
## 356 2020-04-14 London 143
## 357 2020-04-15 London 143
## 358 2020-04-16 London 140
## 359 2020-04-17 London 101
## 360 2020-04-18 London 101
## 361 2020-04-19 London 103
## 362 2020-04-20 London 96
## 363 2020-04-21 London 96
## 364 2020-04-22 London 109
## 365 2020-04-23 London 77
## 366 2020-04-24 London 71
## 367 2020-04-25 London 58
## 368 2020-04-26 London 53
## 369 2020-04-27 London 52
## 370 2020-04-28 London 44
## 371 2020-04-29 London 45
## 372 2020-04-30 London 40
## 373 2020-05-01 London 41
## 374 2020-05-02 London 41
## 375 2020-05-03 London 36
## 376 2020-05-04 London 30
## 377 2020-05-05 London 25
## 378 2020-05-06 London 37
## 379 2020-05-07 London 37
## 380 2020-05-08 London 30
## 381 2020-05-09 London 23
## 382 2020-05-10 London 26
## 383 2020-05-11 London 18
## 384 2020-05-12 London 18
## 385 2020-05-13 London 17
## 386 2020-05-14 London 20
## 387 2020-05-15 London 19
## 388 2020-05-16 London 14
## 389 2020-05-17 London 15
## 390 2020-05-18 London 11
## 391 2020-05-19 London 14
## 392 2020-05-20 London 19
## 393 2020-05-21 London 12
## 394 2020-05-22 London 10
## 395 2020-05-23 London 6
## 396 2020-05-24 London 7
## 397 2020-05-25 London 9
## 398 2020-05-26 London 14
## 399 2020-05-27 London 7
## 400 2020-05-28 London 8
## 401 2020-05-29 London 7
## 402 2020-05-30 London 12
## 403 2020-05-31 London 6
## 404 2020-06-01 London 10
## 405 2020-06-02 London 8
## 406 2020-06-03 London 6
## 407 2020-06-04 London 8
## 408 2020-06-05 London 4
## 409 2020-06-06 London 0
## 410 2020-06-07 London 5
## 411 2020-06-08 London 5
## 412 2020-06-09 London 5
## 413 2020-06-10 London 8
## 414 2020-06-11 London 5
## 415 2020-06-12 London 3
## 416 2020-06-13 London 3
## 417 2020-06-14 London 3
## 418 2020-06-15 London 1
## 419 2020-06-16 London 2
## 420 2020-06-17 London 1
## 421 2020-06-18 London 2
## 422 2020-06-19 London 5
## 423 2020-06-20 London 3
## 424 2020-06-21 London 4
## 425 2020-06-22 London 2
## 426 2020-06-23 London 1
## 427 2020-06-24 London 4
## 428 2020-06-25 London 3
## 429 2020-06-26 London 2
## 430 2020-06-27 London 1
## 431 2020-06-28 London 2
## 432 2020-06-29 London 2
## 433 2020-06-30 London 1
## 434 2020-07-01 London 3
## 435 2020-07-02 London 2
## 436 2020-07-03 London 2
## 437 2020-07-04 London 1
## 438 2020-07-05 London 3
## 439 2020-07-06 London 2
## 440 2020-07-07 London 1
## 441 2020-07-08 London 3
## 442 2020-07-09 London 4
## 443 2020-07-10 London 0
## 444 2020-07-11 London 1
## 445 2020-07-12 London 1
## 446 2020-07-13 London 1
## 447 2020-07-14 London 0
## 448 2020-07-15 London 2
## 449 2020-07-16 London 0
## 450 2020-07-17 London 0
## 451 2020-07-18 London 2
## 452 2020-07-19 London 0
## 453 2020-07-20 London 0
## 454 2020-07-21 London 1
## 455 2020-07-22 London 0
## 456 2020-07-23 London 2
## 457 2020-07-24 London 0
## 458 2020-07-25 London 1
## 459 2020-07-26 London 0
## 460 2020-07-27 London 1
## 461 2020-07-28 London 0
## 462 2020-07-29 London 0
## 463 2020-07-30 London 1
## 464 2020-07-31 London 0
## 465 2020-08-01 London 0
## 466 2020-08-02 London 3
## 467 2020-08-03 London 0
## 468 2020-08-04 London 0
## 469 2020-08-05 London 0
## 470 2020-08-06 London 1
## 471 2020-08-07 London 0
## 472 2020-08-08 London 0
## 473 2020-08-09 London 0
## 474 2020-08-10 London 0
## 475 2020-08-11 London 1
## 476 2020-08-12 London 0
## 477 2020-08-13 London 2
## 478 2020-08-14 London 0
## 479 2020-08-15 London 0
## 480 2020-08-16 London 0
## 481 2020-08-17 London 1
## 482 2020-08-18 London 1
## 483 2020-08-19 London 0
## 484 2020-08-20 London 1
## 485 2020-08-21 London 0
## 486 2020-08-22 London 0
## 487 2020-08-23 London 0
## 488 2020-08-24 London 1
## 489 2020-08-25 London 1
## 490 2020-08-26 London 0
## 491 2020-08-27 London 0
## 492 2020-08-28 London 0
## 493 2020-08-29 London 0
## 494 2020-08-30 London 0
## 495 2020-08-31 London 1
## 496 2020-09-01 London 0
## 497 2020-09-02 London 1
## 498 2020-09-03 London 1
## 499 2020-09-04 London 0
## 500 2020-09-05 London 0
## 501 2020-09-06 London 2
## 502 2020-09-07 London 0
## 503 2020-09-08 London 0
## 504 2020-09-09 London 0
## 505 2020-09-10 London 2
## 506 2020-09-11 London 1
## 507 2020-09-12 London 1
## 508 2020-09-13 London 0
## 509 2020-09-14 London 0
## 510 2020-09-15 London 1
## 511 2020-09-16 London 2
## 512 2020-09-17 London 2
## 513 2020-09-18 London 1
## 514 2020-09-19 London 3
## 515 2020-09-20 London 3
## 516 2020-09-21 London 2
## 517 2020-09-22 London 6
## 518 2020-09-23 London 4
## 519 2020-09-24 London 3
## 520 2020-09-25 London 1
## 521 2020-09-26 London 1
## 522 2020-09-27 London 1
## 523 2020-09-28 London 3
## 524 2020-09-29 London 7
## 525 2020-09-30 London 6
## 526 2020-10-01 London 4
## 527 2020-10-02 London 1
## 528 2020-10-03 London 3
## 529 2020-10-04 London 2
## 530 2020-10-05 London 7
## 531 2020-10-06 London 4
## 532 2020-10-07 London 6
## 533 2020-10-08 London 6
## 534 2020-10-09 London 7
## 535 2020-10-10 London 3
## 536 2020-10-11 London 5
## 537 2020-10-12 London 7
## 538 2020-10-13 London 4
## 539 2020-10-14 London 6
## 540 2020-10-15 London 13
## 541 2020-10-16 London 6
## 542 2020-10-17 London 2
## 543 2020-10-18 London 5
## 544 2020-10-19 London 11
## 545 2020-10-20 London 8
## 546 2020-10-21 London 14
## 547 2020-10-22 London 12
## 548 2020-10-23 London 7
## 549 2020-10-24 London 18
## 550 2020-10-25 London 10
## 551 2020-10-26 London 10
## 552 2020-10-27 London 12
## 553 2020-10-28 London 23
## 554 2020-10-29 London 14
## 555 2020-10-30 London 17
## 556 2020-10-31 London 7
## 557 2020-11-01 London 17
## 558 2020-11-02 London 16
## 559 2020-11-03 London 10
## 560 2020-11-04 London 18
## 561 2020-11-05 London 17
## 562 2020-11-06 London 12
## 563 2020-11-07 London 21
## 564 2020-11-08 London 15
## 565 2020-11-09 London 28
## 566 2020-11-10 London 14
## 567 2020-11-11 London 15
## 568 2020-11-12 London 16
## 569 2020-11-13 London 14
## 570 2020-11-14 London 21
## 571 2020-11-15 London 18
## 572 2020-11-16 London 29
## 573 2020-11-17 London 29
## 574 2020-11-18 London 23
## 575 2020-11-19 London 24
## 576 2020-11-20 London 20
## 577 2020-11-21 London 18
## 578 2020-11-22 London 29
## 579 2020-11-23 London 19
## 580 2020-11-24 London 26
## 581 2020-11-25 London 30
## 582 2020-11-26 London 25
## 583 2020-11-27 London 28
## 584 2020-11-28 London 23
## 585 2020-11-29 London 40
## 586 2020-11-30 London 19
## 587 2020-12-01 London 28
## 588 2020-12-02 London 29
## 589 2020-12-03 London 27
## 590 2020-12-04 London 29
## 591 2020-12-05 London 23
## 592 2020-12-06 London 24
## 593 2020-12-07 London 29
## 594 2020-12-08 London 35
## 595 2020-12-09 London 27
## 596 2020-12-10 London 28
## 597 2020-12-11 London 26
## 598 2020-12-12 London 33
## 599 2020-12-13 London 33
## 600 2020-12-14 London 37
## 601 2020-12-15 London 49
## 602 2020-12-16 London 35
## 603 2020-12-17 London 55
## 604 2020-12-18 London 40
## 605 2020-12-19 London 38
## 606 2020-12-20 London 50
## 607 2020-12-21 London 56
## 608 2020-12-22 London 56
## 609 2020-12-23 London 55
## 610 2020-12-24 London 62
## 611 2020-12-25 London 78
## 612 2020-12-26 London 79
## 613 2020-12-27 London 85
## 614 2020-12-28 London 87
## 615 2020-12-29 London 102
## 616 2020-12-30 London 95
## 617 2020-12-31 London 89
## 618 2021-01-01 London 80
## 619 2021-01-02 London 91
## 620 2021-01-03 London 77
## 621 2021-01-04 London 59
## 622 2021-01-05 London 12
## 623 2020-03-01 Midlands 0
## 624 2020-03-02 Midlands 0
## 625 2020-03-03 Midlands 1
## 626 2020-03-04 Midlands 0
## 627 2020-03-05 Midlands 0
## 628 2020-03-06 Midlands 0
## 629 2020-03-07 Midlands 0
## 630 2020-03-08 Midlands 2
## 631 2020-03-09 Midlands 1
## 632 2020-03-10 Midlands 0
## 633 2020-03-11 Midlands 2
## 634 2020-03-12 Midlands 6
## 635 2020-03-13 Midlands 5
## 636 2020-03-14 Midlands 4
## 637 2020-03-15 Midlands 5
## 638 2020-03-16 Midlands 11
## 639 2020-03-17 Midlands 8
## 640 2020-03-18 Midlands 13
## 641 2020-03-19 Midlands 8
## 642 2020-03-20 Midlands 28
## 643 2020-03-21 Midlands 13
## 644 2020-03-22 Midlands 31
## 645 2020-03-23 Midlands 33
## 646 2020-03-24 Midlands 41
## 647 2020-03-25 Midlands 48
## 648 2020-03-26 Midlands 64
## 649 2020-03-27 Midlands 72
## 650 2020-03-28 Midlands 89
## 651 2020-03-29 Midlands 92
## 652 2020-03-30 Midlands 90
## 653 2020-03-31 Midlands 123
## 654 2020-04-01 Midlands 140
## 655 2020-04-02 Midlands 142
## 656 2020-04-03 Midlands 124
## 657 2020-04-04 Midlands 151
## 658 2020-04-05 Midlands 164
## 659 2020-04-06 Midlands 140
## 660 2020-04-07 Midlands 123
## 661 2020-04-08 Midlands 186
## 662 2020-04-09 Midlands 140
## 663 2020-04-10 Midlands 127
## 664 2020-04-11 Midlands 142
## 665 2020-04-12 Midlands 139
## 666 2020-04-13 Midlands 120
## 667 2020-04-14 Midlands 116
## 668 2020-04-15 Midlands 147
## 669 2020-04-16 Midlands 102
## 670 2020-04-17 Midlands 118
## 671 2020-04-18 Midlands 115
## 672 2020-04-19 Midlands 93
## 673 2020-04-20 Midlands 107
## 674 2020-04-21 Midlands 86
## 675 2020-04-22 Midlands 78
## 676 2020-04-23 Midlands 103
## 677 2020-04-24 Midlands 79
## 678 2020-04-25 Midlands 72
## 679 2020-04-26 Midlands 81
## 680 2020-04-27 Midlands 74
## 681 2020-04-28 Midlands 68
## 682 2020-04-29 Midlands 53
## 683 2020-04-30 Midlands 56
## 684 2020-05-01 Midlands 64
## 685 2020-05-02 Midlands 51
## 686 2020-05-03 Midlands 52
## 687 2020-05-04 Midlands 61
## 688 2020-05-05 Midlands 59
## 689 2020-05-06 Midlands 59
## 690 2020-05-07 Midlands 48
## 691 2020-05-08 Midlands 34
## 692 2020-05-09 Midlands 37
## 693 2020-05-10 Midlands 42
## 694 2020-05-11 Midlands 33
## 695 2020-05-12 Midlands 45
## 696 2020-05-13 Midlands 40
## 697 2020-05-14 Midlands 39
## 698 2020-05-15 Midlands 40
## 699 2020-05-16 Midlands 34
## 700 2020-05-17 Midlands 31
## 701 2020-05-18 Midlands 36
## 702 2020-05-19 Midlands 35
## 703 2020-05-20 Midlands 36
## 704 2020-05-21 Midlands 32
## 705 2020-05-22 Midlands 27
## 706 2020-05-23 Midlands 34
## 707 2020-05-24 Midlands 20
## 708 2020-05-25 Midlands 26
## 709 2020-05-26 Midlands 33
## 710 2020-05-27 Midlands 29
## 711 2020-05-28 Midlands 28
## 712 2020-05-29 Midlands 20
## 713 2020-05-30 Midlands 21
## 714 2020-05-31 Midlands 22
## 715 2020-06-01 Midlands 20
## 716 2020-06-02 Midlands 22
## 717 2020-06-03 Midlands 24
## 718 2020-06-04 Midlands 16
## 719 2020-06-05 Midlands 21
## 720 2020-06-06 Midlands 20
## 721 2020-06-07 Midlands 17
## 722 2020-06-08 Midlands 16
## 723 2020-06-09 Midlands 18
## 724 2020-06-10 Midlands 15
## 725 2020-06-11 Midlands 13
## 726 2020-06-12 Midlands 12
## 727 2020-06-13 Midlands 6
## 728 2020-06-14 Midlands 18
## 729 2020-06-15 Midlands 12
## 730 2020-06-16 Midlands 15
## 731 2020-06-17 Midlands 11
## 732 2020-06-18 Midlands 15
## 733 2020-06-19 Midlands 10
## 734 2020-06-20 Midlands 15
## 735 2020-06-21 Midlands 14
## 736 2020-06-22 Midlands 14
## 737 2020-06-23 Midlands 16
## 738 2020-06-24 Midlands 15
## 739 2020-06-25 Midlands 18
## 740 2020-06-26 Midlands 5
## 741 2020-06-27 Midlands 5
## 742 2020-06-28 Midlands 7
## 743 2020-06-29 Midlands 6
## 744 2020-06-30 Midlands 6
## 745 2020-07-01 Midlands 7
## 746 2020-07-02 Midlands 10
## 747 2020-07-03 Midlands 3
## 748 2020-07-04 Midlands 4
## 749 2020-07-05 Midlands 6
## 750 2020-07-06 Midlands 5
## 751 2020-07-07 Midlands 3
## 752 2020-07-08 Midlands 5
## 753 2020-07-09 Midlands 9
## 754 2020-07-10 Midlands 3
## 755 2020-07-11 Midlands 0
## 756 2020-07-12 Midlands 5
## 757 2020-07-13 Midlands 1
## 758 2020-07-14 Midlands 1
## 759 2020-07-15 Midlands 6
## 760 2020-07-16 Midlands 2
## 761 2020-07-17 Midlands 3
## 762 2020-07-18 Midlands 3
## 763 2020-07-19 Midlands 3
## 764 2020-07-20 Midlands 3
## 765 2020-07-21 Midlands 1
## 766 2020-07-22 Midlands 2
## 767 2020-07-23 Midlands 6
## 768 2020-07-24 Midlands 1
## 769 2020-07-25 Midlands 4
## 770 2020-07-26 Midlands 4
## 771 2020-07-27 Midlands 5
## 772 2020-07-28 Midlands 1
## 773 2020-07-29 Midlands 1
## 774 2020-07-30 Midlands 1
## 775 2020-07-31 Midlands 2
## 776 2020-08-01 Midlands 0
## 777 2020-08-02 Midlands 1
## 778 2020-08-03 Midlands 2
## 779 2020-08-04 Midlands 1
## 780 2020-08-05 Midlands 1
## 781 2020-08-06 Midlands 0
## 782 2020-08-07 Midlands 3
## 783 2020-08-08 Midlands 2
## 784 2020-08-09 Midlands 0
## 785 2020-08-10 Midlands 0
## 786 2020-08-11 Midlands 2
## 787 2020-08-12 Midlands 0
## 788 2020-08-13 Midlands 0
## 789 2020-08-14 Midlands 0
## 790 2020-08-15 Midlands 1
## 791 2020-08-16 Midlands 0
## 792 2020-08-17 Midlands 0
## 793 2020-08-18 Midlands 0
## 794 2020-08-19 Midlands 0
## 795 2020-08-20 Midlands 0
## 796 2020-08-21 Midlands 1
## 797 2020-08-22 Midlands 0
## 798 2020-08-23 Midlands 0
## 799 2020-08-24 Midlands 0
## 800 2020-08-25 Midlands 2
## 801 2020-08-26 Midlands 3
## 802 2020-08-27 Midlands 2
## 803 2020-08-28 Midlands 1
## 804 2020-08-29 Midlands 0
## 805 2020-08-30 Midlands 2
## 806 2020-08-31 Midlands 1
## 807 2020-09-01 Midlands 0
## 808 2020-09-02 Midlands 2
## 809 2020-09-03 Midlands 0
## 810 2020-09-04 Midlands 0
## 811 2020-09-05 Midlands 0
## 812 2020-09-06 Midlands 1
## 813 2020-09-07 Midlands 1
## 814 2020-09-08 Midlands 3
## 815 2020-09-09 Midlands 0
## 816 2020-09-10 Midlands 1
## 817 2020-09-11 Midlands 1
## 818 2020-09-12 Midlands 2
## 819 2020-09-13 Midlands 4
## 820 2020-09-14 Midlands 1
## 821 2020-09-15 Midlands 2
## 822 2020-09-16 Midlands 3
## 823 2020-09-17 Midlands 2
## 824 2020-09-18 Midlands 5
## 825 2020-09-19 Midlands 2
## 826 2020-09-20 Midlands 7
## 827 2020-09-21 Midlands 3
## 828 2020-09-22 Midlands 4
## 829 2020-09-23 Midlands 10
## 830 2020-09-24 Midlands 7
## 831 2020-09-25 Midlands 4
## 832 2020-09-26 Midlands 5
## 833 2020-09-27 Midlands 9
## 834 2020-09-28 Midlands 6
## 835 2020-09-29 Midlands 4
## 836 2020-09-30 Midlands 5
## 837 2020-10-01 Midlands 8
## 838 2020-10-02 Midlands 7
## 839 2020-10-03 Midlands 6
## 840 2020-10-04 Midlands 7
## 841 2020-10-05 Midlands 6
## 842 2020-10-06 Midlands 5
## 843 2020-10-07 Midlands 9
## 844 2020-10-08 Midlands 8
## 845 2020-10-09 Midlands 7
## 846 2020-10-10 Midlands 2
## 847 2020-10-11 Midlands 15
## 848 2020-10-12 Midlands 7
## 849 2020-10-13 Midlands 16
## 850 2020-10-14 Midlands 12
## 851 2020-10-15 Midlands 11
## 852 2020-10-16 Midlands 18
## 853 2020-10-17 Midlands 25
## 854 2020-10-18 Midlands 11
## 855 2020-10-19 Midlands 14
## 856 2020-10-20 Midlands 19
## 857 2020-10-21 Midlands 15
## 858 2020-10-22 Midlands 34
## 859 2020-10-23 Midlands 32
## 860 2020-10-24 Midlands 24
## 861 2020-10-25 Midlands 30
## 862 2020-10-26 Midlands 33
## 863 2020-10-27 Midlands 38
## 864 2020-10-28 Midlands 30
## 865 2020-10-29 Midlands 42
## 866 2020-10-30 Midlands 42
## 867 2020-10-31 Midlands 50
## 868 2020-11-01 Midlands 44
## 869 2020-11-02 Midlands 58
## 870 2020-11-03 Midlands 37
## 871 2020-11-04 Midlands 67
## 872 2020-11-05 Midlands 50
## 873 2020-11-06 Midlands 43
## 874 2020-11-07 Midlands 60
## 875 2020-11-08 Midlands 55
## 876 2020-11-09 Midlands 67
## 877 2020-11-10 Midlands 68
## 878 2020-11-11 Midlands 56
## 879 2020-11-12 Midlands 64
## 880 2020-11-13 Midlands 47
## 881 2020-11-14 Midlands 66
## 882 2020-11-15 Midlands 72
## 883 2020-11-16 Midlands 66
## 884 2020-11-17 Midlands 66
## 885 2020-11-18 Midlands 83
## 886 2020-11-19 Midlands 72
## 887 2020-11-20 Midlands 87
## 888 2020-11-21 Midlands 59
## 889 2020-11-22 Midlands 84
## 890 2020-11-23 Midlands 80
## 891 2020-11-24 Midlands 73
## 892 2020-11-25 Midlands 74
## 893 2020-11-26 Midlands 77
## 894 2020-11-27 Midlands 78
## 895 2020-11-28 Midlands 80
## 896 2020-11-29 Midlands 85
## 897 2020-11-30 Midlands 78
## 898 2020-12-01 Midlands 74
## 899 2020-12-02 Midlands 64
## 900 2020-12-03 Midlands 82
## 901 2020-12-04 Midlands 66
## 902 2020-12-05 Midlands 71
## 903 2020-12-06 Midlands 74
## 904 2020-12-07 Midlands 67
## 905 2020-12-08 Midlands 64
## 906 2020-12-09 Midlands 60
## 907 2020-12-10 Midlands 72
## 908 2020-12-11 Midlands 65
## 909 2020-12-12 Midlands 79
## 910 2020-12-13 Midlands 76
## 911 2020-12-14 Midlands 76
## 912 2020-12-15 Midlands 69
## 913 2020-12-16 Midlands 71
## 914 2020-12-17 Midlands 83
## 915 2020-12-18 Midlands 78
## 916 2020-12-19 Midlands 55
## 917 2020-12-20 Midlands 66
## 918 2020-12-21 Midlands 84
## 919 2020-12-22 Midlands 70
## 920 2020-12-23 Midlands 56
## 921 2020-12-24 Midlands 65
## 922 2020-12-25 Midlands 78
## 923 2020-12-26 Midlands 69
## 924 2020-12-27 Midlands 80
## 925 2020-12-28 Midlands 61
## 926 2020-12-29 Midlands 77
## 927 2020-12-30 Midlands 92
## 928 2020-12-31 Midlands 76
## 929 2021-01-01 Midlands 66
## 930 2021-01-02 Midlands 66
## 931 2021-01-03 Midlands 60
## 932 2021-01-04 Midlands 67
## 933 2021-01-05 Midlands 7
## 934 2020-03-01 North East and Yorkshire 0
## 935 2020-03-02 North East and Yorkshire 0
## 936 2020-03-03 North East and Yorkshire 0
## 937 2020-03-04 North East and Yorkshire 0
## 938 2020-03-05 North East and Yorkshire 0
## 939 2020-03-06 North East and Yorkshire 0
## 940 2020-03-07 North East and Yorkshire 0
## 941 2020-03-08 North East and Yorkshire 0
## 942 2020-03-09 North East and Yorkshire 0
## 943 2020-03-10 North East and Yorkshire 0
## 944 2020-03-11 North East and Yorkshire 0
## 945 2020-03-12 North East and Yorkshire 0
## 946 2020-03-13 North East and Yorkshire 0
## 947 2020-03-14 North East and Yorkshire 0
## 948 2020-03-15 North East and Yorkshire 2
## 949 2020-03-16 North East and Yorkshire 3
## 950 2020-03-17 North East and Yorkshire 1
## 951 2020-03-18 North East and Yorkshire 2
## 952 2020-03-19 North East and Yorkshire 6
## 953 2020-03-20 North East and Yorkshire 5
## 954 2020-03-21 North East and Yorkshire 6
## 955 2020-03-22 North East and Yorkshire 7
## 956 2020-03-23 North East and Yorkshire 9
## 957 2020-03-24 North East and Yorkshire 8
## 958 2020-03-25 North East and Yorkshire 18
## 959 2020-03-26 North East and Yorkshire 21
## 960 2020-03-27 North East and Yorkshire 28
## 961 2020-03-28 North East and Yorkshire 35
## 962 2020-03-29 North East and Yorkshire 38
## 963 2020-03-30 North East and Yorkshire 64
## 964 2020-03-31 North East and Yorkshire 60
## 965 2020-04-01 North East and Yorkshire 67
## 966 2020-04-02 North East and Yorkshire 75
## 967 2020-04-03 North East and Yorkshire 100
## 968 2020-04-04 North East and Yorkshire 105
## 969 2020-04-05 North East and Yorkshire 92
## 970 2020-04-06 North East and Yorkshire 96
## 971 2020-04-07 North East and Yorkshire 102
## 972 2020-04-08 North East and Yorkshire 107
## 973 2020-04-09 North East and Yorkshire 111
## 974 2020-04-10 North East and Yorkshire 117
## 975 2020-04-11 North East and Yorkshire 98
## 976 2020-04-12 North East and Yorkshire 84
## 977 2020-04-13 North East and Yorkshire 94
## 978 2020-04-14 North East and Yorkshire 107
## 979 2020-04-15 North East and Yorkshire 96
## 980 2020-04-16 North East and Yorkshire 103
## 981 2020-04-17 North East and Yorkshire 88
## 982 2020-04-18 North East and Yorkshire 95
## 983 2020-04-19 North East and Yorkshire 88
## 984 2020-04-20 North East and Yorkshire 100
## 985 2020-04-21 North East and Yorkshire 76
## 986 2020-04-22 North East and Yorkshire 84
## 987 2020-04-23 North East and Yorkshire 63
## 988 2020-04-24 North East and Yorkshire 72
## 989 2020-04-25 North East and Yorkshire 69
## 990 2020-04-26 North East and Yorkshire 65
## 991 2020-04-27 North East and Yorkshire 65
## 992 2020-04-28 North East and Yorkshire 57
## 993 2020-04-29 North East and Yorkshire 69
## 994 2020-04-30 North East and Yorkshire 57
## 995 2020-05-01 North East and Yorkshire 64
## 996 2020-05-02 North East and Yorkshire 48
## 997 2020-05-03 North East and Yorkshire 40
## 998 2020-05-04 North East and Yorkshire 49
## 999 2020-05-05 North East and Yorkshire 40
## 1000 2020-05-06 North East and Yorkshire 51
## 1001 2020-05-07 North East and Yorkshire 45
## 1002 2020-05-08 North East and Yorkshire 42
## 1003 2020-05-09 North East and Yorkshire 44
## 1004 2020-05-10 North East and Yorkshire 40
## 1005 2020-05-11 North East and Yorkshire 29
## 1006 2020-05-12 North East and Yorkshire 27
## 1007 2020-05-13 North East and Yorkshire 28
## 1008 2020-05-14 North East and Yorkshire 31
## 1009 2020-05-15 North East and Yorkshire 32
## 1010 2020-05-16 North East and Yorkshire 35
## 1011 2020-05-17 North East and Yorkshire 26
## 1012 2020-05-18 North East and Yorkshire 30
## 1013 2020-05-19 North East and Yorkshire 27
## 1014 2020-05-20 North East and Yorkshire 22
## 1015 2020-05-21 North East and Yorkshire 33
## 1016 2020-05-22 North East and Yorkshire 22
## 1017 2020-05-23 North East and Yorkshire 18
## 1018 2020-05-24 North East and Yorkshire 26
## 1019 2020-05-25 North East and Yorkshire 21
## 1020 2020-05-26 North East and Yorkshire 21
## 1021 2020-05-27 North East and Yorkshire 22
## 1022 2020-05-28 North East and Yorkshire 21
## 1023 2020-05-29 North East and Yorkshire 25
## 1024 2020-05-30 North East and Yorkshire 20
## 1025 2020-05-31 North East and Yorkshire 20
## 1026 2020-06-01 North East and Yorkshire 17
## 1027 2020-06-02 North East and Yorkshire 23
## 1028 2020-06-03 North East and Yorkshire 24
## 1029 2020-06-04 North East and Yorkshire 17
## 1030 2020-06-05 North East and Yorkshire 18
## 1031 2020-06-06 North East and Yorkshire 21
## 1032 2020-06-07 North East and Yorkshire 14
## 1033 2020-06-08 North East and Yorkshire 11
## 1034 2020-06-09 North East and Yorkshire 12
## 1035 2020-06-10 North East and Yorkshire 19
## 1036 2020-06-11 North East and Yorkshire 7
## 1037 2020-06-12 North East and Yorkshire 9
## 1038 2020-06-13 North East and Yorkshire 10
## 1039 2020-06-14 North East and Yorkshire 11
## 1040 2020-06-15 North East and Yorkshire 9
## 1041 2020-06-16 North East and Yorkshire 10
## 1042 2020-06-17 North East and Yorkshire 9
## 1043 2020-06-18 North East and Yorkshire 11
## 1044 2020-06-19 North East and Yorkshire 6
## 1045 2020-06-20 North East and Yorkshire 5
## 1046 2020-06-21 North East and Yorkshire 4
## 1047 2020-06-22 North East and Yorkshire 7
## 1048 2020-06-23 North East and Yorkshire 8
## 1049 2020-06-24 North East and Yorkshire 10
## 1050 2020-06-25 North East and Yorkshire 4
## 1051 2020-06-26 North East and Yorkshire 8
## 1052 2020-06-27 North East and Yorkshire 4
## 1053 2020-06-28 North East and Yorkshire 5
## 1054 2020-06-29 North East and Yorkshire 2
## 1055 2020-06-30 North East and Yorkshire 7
## 1056 2020-07-01 North East and Yorkshire 1
## 1057 2020-07-02 North East and Yorkshire 5
## 1058 2020-07-03 North East and Yorkshire 4
## 1059 2020-07-04 North East and Yorkshire 4
## 1060 2020-07-05 North East and Yorkshire 3
## 1061 2020-07-06 North East and Yorkshire 2
## 1062 2020-07-07 North East and Yorkshire 3
## 1063 2020-07-08 North East and Yorkshire 3
## 1064 2020-07-09 North East and Yorkshire 0
## 1065 2020-07-10 North East and Yorkshire 3
## 1066 2020-07-11 North East and Yorkshire 1
## 1067 2020-07-12 North East and Yorkshire 4
## 1068 2020-07-13 North East and Yorkshire 1
## 1069 2020-07-14 North East and Yorkshire 1
## 1070 2020-07-15 North East and Yorkshire 2
## 1071 2020-07-16 North East and Yorkshire 3
## 1072 2020-07-17 North East and Yorkshire 1
## 1073 2020-07-18 North East and Yorkshire 2
## 1074 2020-07-19 North East and Yorkshire 2
## 1075 2020-07-20 North East and Yorkshire 1
## 1076 2020-07-21 North East and Yorkshire 1
## 1077 2020-07-22 North East and Yorkshire 6
## 1078 2020-07-23 North East and Yorkshire 0
## 1079 2020-07-24 North East and Yorkshire 1
## 1080 2020-07-25 North East and Yorkshire 5
## 1081 2020-07-26 North East and Yorkshire 1
## 1082 2020-07-27 North East and Yorkshire 0
## 1083 2020-07-28 North East and Yorkshire 2
## 1084 2020-07-29 North East and Yorkshire 1
## 1085 2020-07-30 North East and Yorkshire 0
## 1086 2020-07-31 North East and Yorkshire 1
## 1087 2020-08-01 North East and Yorkshire 3
## 1088 2020-08-02 North East and Yorkshire 2
## 1089 2020-08-03 North East and Yorkshire 1
## 1090 2020-08-04 North East and Yorkshire 3
## 1091 2020-08-05 North East and Yorkshire 1
## 1092 2020-08-06 North East and Yorkshire 4
## 1093 2020-08-07 North East and Yorkshire 0
## 1094 2020-08-08 North East and Yorkshire 2
## 1095 2020-08-09 North East and Yorkshire 3
## 1096 2020-08-10 North East and Yorkshire 3
## 1097 2020-08-11 North East and Yorkshire 2
## 1098 2020-08-12 North East and Yorkshire 2
## 1099 2020-08-13 North East and Yorkshire 0
## 1100 2020-08-14 North East and Yorkshire 1
## 1101 2020-08-15 North East and Yorkshire 1
## 1102 2020-08-16 North East and Yorkshire 0
## 1103 2020-08-17 North East and Yorkshire 6
## 1104 2020-08-18 North East and Yorkshire 1
## 1105 2020-08-19 North East and Yorkshire 0
## 1106 2020-08-20 North East and Yorkshire 0
## 1107 2020-08-21 North East and Yorkshire 1
## 1108 2020-08-22 North East and Yorkshire 1
## 1109 2020-08-23 North East and Yorkshire 3
## 1110 2020-08-24 North East and Yorkshire 0
## 1111 2020-08-25 North East and Yorkshire 2
## 1112 2020-08-26 North East and Yorkshire 2
## 1113 2020-08-27 North East and Yorkshire 1
## 1114 2020-08-28 North East and Yorkshire 0
## 1115 2020-08-29 North East and Yorkshire 1
## 1116 2020-08-30 North East and Yorkshire 0
## 1117 2020-08-31 North East and Yorkshire 0
## 1118 2020-09-01 North East and Yorkshire 2
## 1119 2020-09-02 North East and Yorkshire 3
## 1120 2020-09-03 North East and Yorkshire 1
## 1121 2020-09-04 North East and Yorkshire 1
## 1122 2020-09-05 North East and Yorkshire 2
## 1123 2020-09-06 North East and Yorkshire 1
## 1124 2020-09-07 North East and Yorkshire 0
## 1125 2020-09-08 North East and Yorkshire 1
## 1126 2020-09-09 North East and Yorkshire 2
## 1127 2020-09-10 North East and Yorkshire 0
## 1128 2020-09-11 North East and Yorkshire 3
## 1129 2020-09-12 North East and Yorkshire 1
## 1130 2020-09-13 North East and Yorkshire 3
## 1131 2020-09-14 North East and Yorkshire 4
## 1132 2020-09-15 North East and Yorkshire 3
## 1133 2020-09-16 North East and Yorkshire 3
## 1134 2020-09-17 North East and Yorkshire 5
## 1135 2020-09-18 North East and Yorkshire 6
## 1136 2020-09-19 North East and Yorkshire 2
## 1137 2020-09-20 North East and Yorkshire 9
## 1138 2020-09-21 North East and Yorkshire 7
## 1139 2020-09-22 North East and Yorkshire 5
## 1140 2020-09-23 North East and Yorkshire 6
## 1141 2020-09-24 North East and Yorkshire 3
## 1142 2020-09-25 North East and Yorkshire 5
## 1143 2020-09-26 North East and Yorkshire 7
## 1144 2020-09-27 North East and Yorkshire 10
## 1145 2020-09-28 North East and Yorkshire 6
## 1146 2020-09-29 North East and Yorkshire 7
## 1147 2020-09-30 North East and Yorkshire 7
## 1148 2020-10-01 North East and Yorkshire 8
## 1149 2020-10-02 North East and Yorkshire 16
## 1150 2020-10-03 North East and Yorkshire 12
## 1151 2020-10-04 North East and Yorkshire 13
## 1152 2020-10-05 North East and Yorkshire 10
## 1153 2020-10-06 North East and Yorkshire 15
## 1154 2020-10-07 North East and Yorkshire 13
## 1155 2020-10-08 North East and Yorkshire 16
## 1156 2020-10-09 North East and Yorkshire 10
## 1157 2020-10-10 North East and Yorkshire 16
## 1158 2020-10-11 North East and Yorkshire 16
## 1159 2020-10-12 North East and Yorkshire 15
## 1160 2020-10-13 North East and Yorkshire 21
## 1161 2020-10-14 North East and Yorkshire 20
## 1162 2020-10-15 North East and Yorkshire 23
## 1163 2020-10-16 North East and Yorkshire 24
## 1164 2020-10-17 North East and Yorkshire 34
## 1165 2020-10-18 North East and Yorkshire 22
## 1166 2020-10-19 North East and Yorkshire 34
## 1167 2020-10-20 North East and Yorkshire 36
## 1168 2020-10-21 North East and Yorkshire 42
## 1169 2020-10-22 North East and Yorkshire 33
## 1170 2020-10-23 North East and Yorkshire 31
## 1171 2020-10-24 North East and Yorkshire 34
## 1172 2020-10-25 North East and Yorkshire 35
## 1173 2020-10-26 North East and Yorkshire 44
## 1174 2020-10-27 North East and Yorkshire 45
## 1175 2020-10-28 North East and Yorkshire 38
## 1176 2020-10-29 North East and Yorkshire 51
## 1177 2020-10-30 North East and Yorkshire 48
## 1178 2020-10-31 North East and Yorkshire 58
## 1179 2020-11-01 North East and Yorkshire 48
## 1180 2020-11-02 North East and Yorkshire 50
## 1181 2020-11-03 North East and Yorkshire 48
## 1182 2020-11-04 North East and Yorkshire 57
## 1183 2020-11-05 North East and Yorkshire 57
## 1184 2020-11-06 North East and Yorkshire 57
## 1185 2020-11-07 North East and Yorkshire 75
## 1186 2020-11-08 North East and Yorkshire 61
## 1187 2020-11-09 North East and Yorkshire 87
## 1188 2020-11-10 North East and Yorkshire 65
## 1189 2020-11-11 North East and Yorkshire 59
## 1190 2020-11-12 North East and Yorkshire 77
## 1191 2020-11-13 North East and Yorkshire 78
## 1192 2020-11-14 North East and Yorkshire 72
## 1193 2020-11-15 North East and Yorkshire 76
## 1194 2020-11-16 North East and Yorkshire 51
## 1195 2020-11-17 North East and Yorkshire 68
## 1196 2020-11-18 North East and Yorkshire 79
## 1197 2020-11-19 North East and Yorkshire 72
## 1198 2020-11-20 North East and Yorkshire 75
## 1199 2020-11-21 North East and Yorkshire 54
## 1200 2020-11-22 North East and Yorkshire 80
## 1201 2020-11-23 North East and Yorkshire 82
## 1202 2020-11-24 North East and Yorkshire 81
## 1203 2020-11-25 North East and Yorkshire 68
## 1204 2020-11-26 North East and Yorkshire 63
## 1205 2020-11-27 North East and Yorkshire 62
## 1206 2020-11-28 North East and Yorkshire 73
## 1207 2020-11-29 North East and Yorkshire 61
## 1208 2020-11-30 North East and Yorkshire 54
## 1209 2020-12-01 North East and Yorkshire 42
## 1210 2020-12-02 North East and Yorkshire 57
## 1211 2020-12-03 North East and Yorkshire 70
## 1212 2020-12-04 North East and Yorkshire 63
## 1213 2020-12-05 North East and Yorkshire 48
## 1214 2020-12-06 North East and Yorkshire 63
## 1215 2020-12-07 North East and Yorkshire 49
## 1216 2020-12-08 North East and Yorkshire 54
## 1217 2020-12-09 North East and Yorkshire 49
## 1218 2020-12-10 North East and Yorkshire 54
## 1219 2020-12-11 North East and Yorkshire 54
## 1220 2020-12-12 North East and Yorkshire 54
## 1221 2020-12-13 North East and Yorkshire 51
## 1222 2020-12-14 North East and Yorkshire 49
## 1223 2020-12-15 North East and Yorkshire 52
## 1224 2020-12-16 North East and Yorkshire 39
## 1225 2020-12-17 North East and Yorkshire 48
## 1226 2020-12-18 North East and Yorkshire 58
## 1227 2020-12-19 North East and Yorkshire 48
## 1228 2020-12-20 North East and Yorkshire 52
## 1229 2020-12-21 North East and Yorkshire 33
## 1230 2020-12-22 North East and Yorkshire 55
## 1231 2020-12-23 North East and Yorkshire 55
## 1232 2020-12-24 North East and Yorkshire 48
## 1233 2020-12-25 North East and Yorkshire 53
## 1234 2020-12-26 North East and Yorkshire 63
## 1235 2020-12-27 North East and Yorkshire 65
## 1236 2020-12-28 North East and Yorkshire 58
## 1237 2020-12-29 North East and Yorkshire 54
## 1238 2020-12-30 North East and Yorkshire 39
## 1239 2020-12-31 North East and Yorkshire 45
## 1240 2021-01-01 North East and Yorkshire 64
## 1241 2021-01-02 North East and Yorkshire 51
## 1242 2021-01-03 North East and Yorkshire 43
## 1243 2021-01-04 North East and Yorkshire 40
## 1244 2021-01-05 North East and Yorkshire 11
## 1245 2020-03-01 North West 0
## 1246 2020-03-02 North West 0
## 1247 2020-03-03 North West 0
## 1248 2020-03-04 North West 0
## 1249 2020-03-05 North West 1
## 1250 2020-03-06 North West 0
## 1251 2020-03-07 North West 0
## 1252 2020-03-08 North West 1
## 1253 2020-03-09 North West 0
## 1254 2020-03-10 North West 0
## 1255 2020-03-11 North West 0
## 1256 2020-03-12 North West 2
## 1257 2020-03-13 North West 3
## 1258 2020-03-14 North West 1
## 1259 2020-03-15 North West 4
## 1260 2020-03-16 North West 2
## 1261 2020-03-17 North West 4
## 1262 2020-03-18 North West 6
## 1263 2020-03-19 North West 7
## 1264 2020-03-20 North West 10
## 1265 2020-03-21 North West 11
## 1266 2020-03-22 North West 13
## 1267 2020-03-23 North West 15
## 1268 2020-03-24 North West 21
## 1269 2020-03-25 North West 21
## 1270 2020-03-26 North West 29
## 1271 2020-03-27 North West 36
## 1272 2020-03-28 North West 28
## 1273 2020-03-29 North West 46
## 1274 2020-03-30 North West 67
## 1275 2020-03-31 North West 52
## 1276 2020-04-01 North West 86
## 1277 2020-04-02 North West 96
## 1278 2020-04-03 North West 95
## 1279 2020-04-04 North West 98
## 1280 2020-04-05 North West 102
## 1281 2020-04-06 North West 100
## 1282 2020-04-07 North West 136
## 1283 2020-04-08 North West 127
## 1284 2020-04-09 North West 119
## 1285 2020-04-10 North West 117
## 1286 2020-04-11 North West 138
## 1287 2020-04-12 North West 125
## 1288 2020-04-13 North West 130
## 1289 2020-04-14 North West 130
## 1290 2020-04-15 North West 114
## 1291 2020-04-16 North West 135
## 1292 2020-04-17 North West 98
## 1293 2020-04-18 North West 113
## 1294 2020-04-19 North West 71
## 1295 2020-04-20 North West 83
## 1296 2020-04-21 North West 76
## 1297 2020-04-22 North West 86
## 1298 2020-04-23 North West 85
## 1299 2020-04-24 North West 66
## 1300 2020-04-25 North West 66
## 1301 2020-04-26 North West 55
## 1302 2020-04-27 North West 54
## 1303 2020-04-28 North West 57
## 1304 2020-04-29 North West 63
## 1305 2020-04-30 North West 60
## 1306 2020-05-01 North West 45
## 1307 2020-05-02 North West 56
## 1308 2020-05-03 North West 55
## 1309 2020-05-04 North West 48
## 1310 2020-05-05 North West 48
## 1311 2020-05-06 North West 44
## 1312 2020-05-07 North West 49
## 1313 2020-05-08 North West 42
## 1314 2020-05-09 North West 31
## 1315 2020-05-10 North West 42
## 1316 2020-05-11 North West 35
## 1317 2020-05-12 North West 38
## 1318 2020-05-13 North West 25
## 1319 2020-05-14 North West 26
## 1320 2020-05-15 North West 33
## 1321 2020-05-16 North West 32
## 1322 2020-05-17 North West 24
## 1323 2020-05-18 North West 31
## 1324 2020-05-19 North West 35
## 1325 2020-05-20 North West 27
## 1326 2020-05-21 North West 28
## 1327 2020-05-22 North West 26
## 1328 2020-05-23 North West 31
## 1329 2020-05-24 North West 26
## 1330 2020-05-25 North West 31
## 1331 2020-05-26 North West 27
## 1332 2020-05-27 North West 27
## 1333 2020-05-28 North West 28
## 1334 2020-05-29 North West 20
## 1335 2020-05-30 North West 19
## 1336 2020-05-31 North West 13
## 1337 2020-06-01 North West 12
## 1338 2020-06-02 North West 27
## 1339 2020-06-03 North West 22
## 1340 2020-06-04 North West 22
## 1341 2020-06-05 North West 16
## 1342 2020-06-06 North West 26
## 1343 2020-06-07 North West 20
## 1344 2020-06-08 North West 23
## 1345 2020-06-09 North West 17
## 1346 2020-06-10 North West 16
## 1347 2020-06-11 North West 16
## 1348 2020-06-12 North West 11
## 1349 2020-06-13 North West 10
## 1350 2020-06-14 North West 15
## 1351 2020-06-15 North West 16
## 1352 2020-06-16 North West 16
## 1353 2020-06-17 North West 13
## 1354 2020-06-18 North West 14
## 1355 2020-06-19 North West 7
## 1356 2020-06-20 North West 11
## 1357 2020-06-21 North West 8
## 1358 2020-06-22 North West 11
## 1359 2020-06-23 North West 13
## 1360 2020-06-24 North West 13
## 1361 2020-06-25 North West 15
## 1362 2020-06-26 North West 6
## 1363 2020-06-27 North West 7
## 1364 2020-06-28 North West 9
## 1365 2020-06-29 North West 9
## 1366 2020-06-30 North West 7
## 1367 2020-07-01 North West 3
## 1368 2020-07-02 North West 6
## 1369 2020-07-03 North West 7
## 1370 2020-07-04 North West 4
## 1371 2020-07-05 North West 6
## 1372 2020-07-06 North West 9
## 1373 2020-07-07 North West 8
## 1374 2020-07-08 North West 5
## 1375 2020-07-09 North West 10
## 1376 2020-07-10 North West 2
## 1377 2020-07-11 North West 5
## 1378 2020-07-12 North West 0
## 1379 2020-07-13 North West 6
## 1380 2020-07-14 North West 4
## 1381 2020-07-15 North West 5
## 1382 2020-07-16 North West 2
## 1383 2020-07-17 North West 4
## 1384 2020-07-18 North West 5
## 1385 2020-07-19 North West 3
## 1386 2020-07-20 North West 0
## 1387 2020-07-21 North West 2
## 1388 2020-07-22 North West 3
## 1389 2020-07-23 North West 3
## 1390 2020-07-24 North West 1
## 1391 2020-07-25 North West 1
## 1392 2020-07-26 North West 3
## 1393 2020-07-27 North West 1
## 1394 2020-07-28 North West 1
## 1395 2020-07-29 North West 2
## 1396 2020-07-30 North West 2
## 1397 2020-07-31 North West 0
## 1398 2020-08-01 North West 2
## 1399 2020-08-02 North West 1
## 1400 2020-08-03 North West 8
## 1401 2020-08-04 North West 3
## 1402 2020-08-05 North West 2
## 1403 2020-08-06 North West 2
## 1404 2020-08-07 North West 2
## 1405 2020-08-08 North West 2
## 1406 2020-08-09 North West 3
## 1407 2020-08-10 North West 2
## 1408 2020-08-11 North West 3
## 1409 2020-08-12 North West 0
## 1410 2020-08-13 North West 2
## 1411 2020-08-14 North West 2
## 1412 2020-08-15 North West 6
## 1413 2020-08-16 North West 2
## 1414 2020-08-17 North West 1
## 1415 2020-08-18 North West 2
## 1416 2020-08-19 North West 1
## 1417 2020-08-20 North West 1
## 1418 2020-08-21 North West 4
## 1419 2020-08-22 North West 3
## 1420 2020-08-23 North West 5
## 1421 2020-08-24 North West 4
## 1422 2020-08-25 North West 3
## 1423 2020-08-26 North West 4
## 1424 2020-08-27 North West 1
## 1425 2020-08-28 North West 2
## 1426 2020-08-29 North West 0
## 1427 2020-08-30 North West 2
## 1428 2020-08-31 North West 3
## 1429 2020-09-01 North West 0
## 1430 2020-09-02 North West 2
## 1431 2020-09-03 North West 1
## 1432 2020-09-04 North West 3
## 1433 2020-09-05 North West 6
## 1434 2020-09-06 North West 1
## 1435 2020-09-07 North West 8
## 1436 2020-09-08 North West 6
## 1437 2020-09-09 North West 5
## 1438 2020-09-10 North West 5
## 1439 2020-09-11 North West 1
## 1440 2020-09-12 North West 4
## 1441 2020-09-13 North West 2
## 1442 2020-09-14 North West 4
## 1443 2020-09-15 North West 4
## 1444 2020-09-16 North West 6
## 1445 2020-09-17 North West 7
## 1446 2020-09-18 North West 6
## 1447 2020-09-19 North West 3
## 1448 2020-09-20 North West 2
## 1449 2020-09-21 North West 2
## 1450 2020-09-22 North West 9
## 1451 2020-09-23 North West 14
## 1452 2020-09-24 North West 10
## 1453 2020-09-25 North West 8
## 1454 2020-09-26 North West 14
## 1455 2020-09-27 North West 11
## 1456 2020-09-28 North West 15
## 1457 2020-09-29 North West 12
## 1458 2020-09-30 North West 17
## 1459 2020-10-01 North West 17
## 1460 2020-10-02 North West 20
## 1461 2020-10-03 North West 15
## 1462 2020-10-04 North West 15
## 1463 2020-10-05 North West 15
## 1464 2020-10-06 North West 20
## 1465 2020-10-07 North West 20
## 1466 2020-10-08 North West 22
## 1467 2020-10-09 North West 23
## 1468 2020-10-10 North West 31
## 1469 2020-10-11 North West 31
## 1470 2020-10-12 North West 35
## 1471 2020-10-13 North West 26
## 1472 2020-10-14 North West 35
## 1473 2020-10-15 North West 36
## 1474 2020-10-16 North West 34
## 1475 2020-10-17 North West 52
## 1476 2020-10-18 North West 40
## 1477 2020-10-19 North West 43
## 1478 2020-10-20 North West 48
## 1479 2020-10-21 North West 51
## 1480 2020-10-22 North West 49
## 1481 2020-10-23 North West 50
## 1482 2020-10-24 North West 51
## 1483 2020-10-25 North West 63
## 1484 2020-10-26 North West 53
## 1485 2020-10-27 North West 49
## 1486 2020-10-28 North West 57
## 1487 2020-10-29 North West 74
## 1488 2020-10-30 North West 73
## 1489 2020-10-31 North West 63
## 1490 2020-11-01 North West 76
## 1491 2020-11-02 North West 65
## 1492 2020-11-03 North West 76
## 1493 2020-11-04 North West 64
## 1494 2020-11-05 North West 67
## 1495 2020-11-06 North West 75
## 1496 2020-11-07 North West 79
## 1497 2020-11-08 North West 83
## 1498 2020-11-09 North West 82
## 1499 2020-11-10 North West 68
## 1500 2020-11-11 North West 61
## 1501 2020-11-12 North West 64
## 1502 2020-11-13 North West 81
## 1503 2020-11-14 North West 61
## 1504 2020-11-15 North West 75
## 1505 2020-11-16 North West 74
## 1506 2020-11-17 North West 73
## 1507 2020-11-18 North West 70
## 1508 2020-11-19 North West 67
## 1509 2020-11-20 North West 52
## 1510 2020-11-21 North West 68
## 1511 2020-11-22 North West 52
## 1512 2020-11-23 North West 54
## 1513 2020-11-24 North West 64
## 1514 2020-11-25 North West 65
## 1515 2020-11-26 North West 53
## 1516 2020-11-27 North West 51
## 1517 2020-11-28 North West 46
## 1518 2020-11-29 North West 54
## 1519 2020-11-30 North West 48
## 1520 2020-12-01 North West 53
## 1521 2020-12-02 North West 48
## 1522 2020-12-03 North West 46
## 1523 2020-12-04 North West 46
## 1524 2020-12-05 North West 37
## 1525 2020-12-06 North West 43
## 1526 2020-12-07 North West 50
## 1527 2020-12-08 North West 48
## 1528 2020-12-09 North West 47
## 1529 2020-12-10 North West 47
## 1530 2020-12-11 North West 41
## 1531 2020-12-12 North West 47
## 1532 2020-12-13 North West 39
## 1533 2020-12-14 North West 49
## 1534 2020-12-15 North West 33
## 1535 2020-12-16 North West 40
## 1536 2020-12-17 North West 25
## 1537 2020-12-18 North West 47
## 1538 2020-12-19 North West 45
## 1539 2020-12-20 North West 36
## 1540 2020-12-21 North West 49
## 1541 2020-12-22 North West 52
## 1542 2020-12-23 North West 48
## 1543 2020-12-24 North West 55
## 1544 2020-12-25 North West 51
## 1545 2020-12-26 North West 55
## 1546 2020-12-27 North West 50
## 1547 2020-12-28 North West 46
## 1548 2020-12-29 North West 45
## 1549 2020-12-30 North West 48
## 1550 2020-12-31 North West 54
## 1551 2021-01-01 North West 46
## 1552 2021-01-02 North West 47
## 1553 2021-01-03 North West 50
## 1554 2021-01-04 North West 34
## 1555 2021-01-05 North West 12
## 1556 2020-03-01 South East 0
## 1557 2020-03-02 South East 0
## 1558 2020-03-03 South East 1
## 1559 2020-03-04 South East 0
## 1560 2020-03-05 South East 1
## 1561 2020-03-06 South East 0
## 1562 2020-03-07 South East 0
## 1563 2020-03-08 South East 1
## 1564 2020-03-09 South East 1
## 1565 2020-03-10 South East 1
## 1566 2020-03-11 South East 1
## 1567 2020-03-12 South East 0
## 1568 2020-03-13 South East 1
## 1569 2020-03-14 South East 1
## 1570 2020-03-15 South East 5
## 1571 2020-03-16 South East 8
## 1572 2020-03-17 South East 7
## 1573 2020-03-18 South East 10
## 1574 2020-03-19 South East 9
## 1575 2020-03-20 South East 13
## 1576 2020-03-21 South East 7
## 1577 2020-03-22 South East 25
## 1578 2020-03-23 South East 20
## 1579 2020-03-24 South East 22
## 1580 2020-03-25 South East 29
## 1581 2020-03-26 South East 35
## 1582 2020-03-27 South East 36
## 1583 2020-03-28 South East 36
## 1584 2020-03-29 South East 55
## 1585 2020-03-30 South East 58
## 1586 2020-03-31 South East 65
## 1587 2020-04-01 South East 66
## 1588 2020-04-02 South East 55
## 1589 2020-04-03 South East 72
## 1590 2020-04-04 South East 80
## 1591 2020-04-05 South East 82
## 1592 2020-04-06 South East 88
## 1593 2020-04-07 South East 100
## 1594 2020-04-08 South East 83
## 1595 2020-04-09 South East 104
## 1596 2020-04-10 South East 88
## 1597 2020-04-11 South East 88
## 1598 2020-04-12 South East 88
## 1599 2020-04-13 South East 84
## 1600 2020-04-14 South East 65
## 1601 2020-04-15 South East 72
## 1602 2020-04-16 South East 56
## 1603 2020-04-17 South East 86
## 1604 2020-04-18 South East 57
## 1605 2020-04-19 South East 70
## 1606 2020-04-20 South East 87
## 1607 2020-04-21 South East 51
## 1608 2020-04-22 South East 54
## 1609 2020-04-23 South East 57
## 1610 2020-04-24 South East 64
## 1611 2020-04-25 South East 51
## 1612 2020-04-26 South East 51
## 1613 2020-04-27 South East 41
## 1614 2020-04-28 South East 40
## 1615 2020-04-29 South East 47
## 1616 2020-04-30 South East 29
## 1617 2020-05-01 South East 37
## 1618 2020-05-02 South East 36
## 1619 2020-05-03 South East 17
## 1620 2020-05-04 South East 35
## 1621 2020-05-05 South East 29
## 1622 2020-05-06 South East 25
## 1623 2020-05-07 South East 27
## 1624 2020-05-08 South East 26
## 1625 2020-05-09 South East 28
## 1626 2020-05-10 South East 19
## 1627 2020-05-11 South East 25
## 1628 2020-05-12 South East 27
## 1629 2020-05-13 South East 18
## 1630 2020-05-14 South East 32
## 1631 2020-05-15 South East 25
## 1632 2020-05-16 South East 22
## 1633 2020-05-17 South East 18
## 1634 2020-05-18 South East 22
## 1635 2020-05-19 South East 12
## 1636 2020-05-20 South East 22
## 1637 2020-05-21 South East 15
## 1638 2020-05-22 South East 17
## 1639 2020-05-23 South East 21
## 1640 2020-05-24 South East 17
## 1641 2020-05-25 South East 13
## 1642 2020-05-26 South East 19
## 1643 2020-05-27 South East 19
## 1644 2020-05-28 South East 12
## 1645 2020-05-29 South East 22
## 1646 2020-05-30 South East 8
## 1647 2020-05-31 South East 12
## 1648 2020-06-01 South East 11
## 1649 2020-06-02 South East 13
## 1650 2020-06-03 South East 18
## 1651 2020-06-04 South East 11
## 1652 2020-06-05 South East 11
## 1653 2020-06-06 South East 10
## 1654 2020-06-07 South East 12
## 1655 2020-06-08 South East 8
## 1656 2020-06-09 South East 10
## 1657 2020-06-10 South East 11
## 1658 2020-06-11 South East 5
## 1659 2020-06-12 South East 6
## 1660 2020-06-13 South East 7
## 1661 2020-06-14 South East 7
## 1662 2020-06-15 South East 8
## 1663 2020-06-16 South East 14
## 1664 2020-06-17 South East 9
## 1665 2020-06-18 South East 4
## 1666 2020-06-19 South East 7
## 1667 2020-06-20 South East 5
## 1668 2020-06-21 South East 3
## 1669 2020-06-22 South East 2
## 1670 2020-06-23 South East 9
## 1671 2020-06-24 South East 7
## 1672 2020-06-25 South East 5
## 1673 2020-06-26 South East 8
## 1674 2020-06-27 South East 9
## 1675 2020-06-28 South East 6
## 1676 2020-06-29 South East 5
## 1677 2020-06-30 South East 5
## 1678 2020-07-01 South East 2
## 1679 2020-07-02 South East 8
## 1680 2020-07-03 South East 3
## 1681 2020-07-04 South East 6
## 1682 2020-07-05 South East 5
## 1683 2020-07-06 South East 4
## 1684 2020-07-07 South East 6
## 1685 2020-07-08 South East 3
## 1686 2020-07-09 South East 7
## 1687 2020-07-10 South East 3
## 1688 2020-07-11 South East 4
## 1689 2020-07-12 South East 5
## 1690 2020-07-13 South East 5
## 1691 2020-07-14 South East 5
## 1692 2020-07-15 South East 6
## 1693 2020-07-16 South East 3
## 1694 2020-07-17 South East 1
## 1695 2020-07-18 South East 5
## 1696 2020-07-19 South East 2
## 1697 2020-07-20 South East 6
## 1698 2020-07-21 South East 4
## 1699 2020-07-22 South East 2
## 1700 2020-07-23 South East 3
## 1701 2020-07-24 South East 1
## 1702 2020-07-25 South East 1
## 1703 2020-07-26 South East 3
## 1704 2020-07-27 South East 1
## 1705 2020-07-28 South East 3
## 1706 2020-07-29 South East 2
## 1707 2020-07-30 South East 3
## 1708 2020-07-31 South East 1
## 1709 2020-08-01 South East 2
## 1710 2020-08-02 South East 4
## 1711 2020-08-03 South East 0
## 1712 2020-08-04 South East 0
## 1713 2020-08-05 South East 0
## 1714 2020-08-06 South East 2
## 1715 2020-08-07 South East 0
## 1716 2020-08-08 South East 2
## 1717 2020-08-09 South East 0
## 1718 2020-08-10 South East 2
## 1719 2020-08-11 South East 1
## 1720 2020-08-12 South East 1
## 1721 2020-08-13 South East 0
## 1722 2020-08-14 South East 0
## 1723 2020-08-15 South East 2
## 1724 2020-08-16 South East 1
## 1725 2020-08-17 South East 0
## 1726 2020-08-18 South East 2
## 1727 2020-08-19 South East 1
## 1728 2020-08-20 South East 0
## 1729 2020-08-21 South East 0
## 1730 2020-08-22 South East 0
## 1731 2020-08-23 South East 1
## 1732 2020-08-24 South East 0
## 1733 2020-08-25 South East 1
## 1734 2020-08-26 South East 0
## 1735 2020-08-27 South East 1
## 1736 2020-08-28 South East 2
## 1737 2020-08-29 South East 1
## 1738 2020-08-30 South East 0
## 1739 2020-08-31 South East 2
## 1740 2020-09-01 South East 1
## 1741 2020-09-02 South East 1
## 1742 2020-09-03 South East 0
## 1743 2020-09-04 South East 1
## 1744 2020-09-05 South East 0
## 1745 2020-09-06 South East 1
## 1746 2020-09-07 South East 0
## 1747 2020-09-08 South East 0
## 1748 2020-09-09 South East 0
## 1749 2020-09-10 South East 1
## 1750 2020-09-11 South East 1
## 1751 2020-09-12 South East 0
## 1752 2020-09-13 South East 3
## 1753 2020-09-14 South East 1
## 1754 2020-09-15 South East 2
## 1755 2020-09-16 South East 2
## 1756 2020-09-17 South East 3
## 1757 2020-09-18 South East 1
## 1758 2020-09-19 South East 1
## 1759 2020-09-20 South East 0
## 1760 2020-09-21 South East 3
## 1761 2020-09-22 South East 0
## 1762 2020-09-23 South East 2
## 1763 2020-09-24 South East 1
## 1764 2020-09-25 South East 3
## 1765 2020-09-26 South East 2
## 1766 2020-09-27 South East 2
## 1767 2020-09-28 South East 6
## 1768 2020-09-29 South East 3
## 1769 2020-09-30 South East 4
## 1770 2020-10-01 South East 4
## 1771 2020-10-02 South East 2
## 1772 2020-10-03 South East 1
## 1773 2020-10-04 South East 1
## 1774 2020-10-05 South East 2
## 1775 2020-10-06 South East 1
## 1776 2020-10-07 South East 4
## 1777 2020-10-08 South East 1
## 1778 2020-10-09 South East 1
## 1779 2020-10-10 South East 3
## 1780 2020-10-11 South East 3
## 1781 2020-10-12 South East 4
## 1782 2020-10-13 South East 2
## 1783 2020-10-14 South East 2
## 1784 2020-10-15 South East 3
## 1785 2020-10-16 South East 2
## 1786 2020-10-17 South East 3
## 1787 2020-10-18 South East 4
## 1788 2020-10-19 South East 7
## 1789 2020-10-20 South East 8
## 1790 2020-10-21 South East 9
## 1791 2020-10-22 South East 5
## 1792 2020-10-23 South East 7
## 1793 2020-10-24 South East 5
## 1794 2020-10-25 South East 9
## 1795 2020-10-26 South East 13
## 1796 2020-10-27 South East 10
## 1797 2020-10-28 South East 10
## 1798 2020-10-29 South East 7
## 1799 2020-10-30 South East 6
## 1800 2020-10-31 South East 15
## 1801 2020-11-01 South East 18
## 1802 2020-11-02 South East 13
## 1803 2020-11-03 South East 16
## 1804 2020-11-04 South East 10
## 1805 2020-11-05 South East 10
## 1806 2020-11-06 South East 16
## 1807 2020-11-07 South East 17
## 1808 2020-11-08 South East 18
## 1809 2020-11-09 South East 19
## 1810 2020-11-10 South East 20
## 1811 2020-11-11 South East 19
## 1812 2020-11-12 South East 20
## 1813 2020-11-13 South East 12
## 1814 2020-11-14 South East 24
## 1815 2020-11-15 South East 25
## 1816 2020-11-16 South East 22
## 1817 2020-11-17 South East 23
## 1818 2020-11-18 South East 26
## 1819 2020-11-19 South East 21
## 1820 2020-11-20 South East 18
## 1821 2020-11-21 South East 23
## 1822 2020-11-22 South East 30
## 1823 2020-11-23 South East 28
## 1824 2020-11-24 South East 26
## 1825 2020-11-25 South East 42
## 1826 2020-11-26 South East 30
## 1827 2020-11-27 South East 31
## 1828 2020-11-28 South East 24
## 1829 2020-11-29 South East 37
## 1830 2020-11-30 South East 22
## 1831 2020-12-01 South East 29
## 1832 2020-12-02 South East 33
## 1833 2020-12-03 South East 36
## 1834 2020-12-04 South East 40
## 1835 2020-12-05 South East 34
## 1836 2020-12-06 South East 31
## 1837 2020-12-07 South East 24
## 1838 2020-12-08 South East 43
## 1839 2020-12-09 South East 44
## 1840 2020-12-10 South East 37
## 1841 2020-12-11 South East 48
## 1842 2020-12-12 South East 38
## 1843 2020-12-13 South East 37
## 1844 2020-12-14 South East 34
## 1845 2020-12-15 South East 47
## 1846 2020-12-16 South East 41
## 1847 2020-12-17 South East 46
## 1848 2020-12-18 South East 39
## 1849 2020-12-19 South East 36
## 1850 2020-12-20 South East 45
## 1851 2020-12-21 South East 55
## 1852 2020-12-22 South East 51
## 1853 2020-12-23 South East 60
## 1854 2020-12-24 South East 44
## 1855 2020-12-25 South East 60
## 1856 2020-12-26 South East 64
## 1857 2020-12-27 South East 67
## 1858 2020-12-28 South East 75
## 1859 2020-12-29 South East 71
## 1860 2020-12-30 South East 75
## 1861 2020-12-31 South East 69
## 1862 2021-01-01 South East 47
## 1863 2021-01-02 South East 67
## 1864 2021-01-03 South East 49
## 1865 2021-01-04 South East 54
## 1866 2021-01-05 South East 8
## 1867 2020-03-01 South West 0
## 1868 2020-03-02 South West 0
## 1869 2020-03-03 South West 0
## 1870 2020-03-04 South West 0
## 1871 2020-03-05 South West 0
## 1872 2020-03-06 South West 0
## 1873 2020-03-07 South West 0
## 1874 2020-03-08 South West 0
## 1875 2020-03-09 South West 0
## 1876 2020-03-10 South West 0
## 1877 2020-03-11 South West 1
## 1878 2020-03-12 South West 0
## 1879 2020-03-13 South West 0
## 1880 2020-03-14 South West 1
## 1881 2020-03-15 South West 0
## 1882 2020-03-16 South West 0
## 1883 2020-03-17 South West 2
## 1884 2020-03-18 South West 2
## 1885 2020-03-19 South West 4
## 1886 2020-03-20 South West 3
## 1887 2020-03-21 South West 6
## 1888 2020-03-22 South West 7
## 1889 2020-03-23 South West 8
## 1890 2020-03-24 South West 7
## 1891 2020-03-25 South West 9
## 1892 2020-03-26 South West 11
## 1893 2020-03-27 South West 13
## 1894 2020-03-28 South West 21
## 1895 2020-03-29 South West 18
## 1896 2020-03-30 South West 23
## 1897 2020-03-31 South West 23
## 1898 2020-04-01 South West 21
## 1899 2020-04-02 South West 23
## 1900 2020-04-03 South West 30
## 1901 2020-04-04 South West 42
## 1902 2020-04-05 South West 32
## 1903 2020-04-06 South West 34
## 1904 2020-04-07 South West 39
## 1905 2020-04-08 South West 47
## 1906 2020-04-09 South West 24
## 1907 2020-04-10 South West 46
## 1908 2020-04-11 South West 43
## 1909 2020-04-12 South West 23
## 1910 2020-04-13 South West 27
## 1911 2020-04-14 South West 24
## 1912 2020-04-15 South West 32
## 1913 2020-04-16 South West 29
## 1914 2020-04-17 South West 33
## 1915 2020-04-18 South West 25
## 1916 2020-04-19 South West 31
## 1917 2020-04-20 South West 26
## 1918 2020-04-21 South West 26
## 1919 2020-04-22 South West 23
## 1920 2020-04-23 South West 17
## 1921 2020-04-24 South West 19
## 1922 2020-04-25 South West 15
## 1923 2020-04-26 South West 27
## 1924 2020-04-27 South West 13
## 1925 2020-04-28 South West 17
## 1926 2020-04-29 South West 15
## 1927 2020-04-30 South West 26
## 1928 2020-05-01 South West 6
## 1929 2020-05-02 South West 7
## 1930 2020-05-03 South West 10
## 1931 2020-05-04 South West 17
## 1932 2020-05-05 South West 14
## 1933 2020-05-06 South West 19
## 1934 2020-05-07 South West 16
## 1935 2020-05-08 South West 6
## 1936 2020-05-09 South West 11
## 1937 2020-05-10 South West 5
## 1938 2020-05-11 South West 8
## 1939 2020-05-12 South West 7
## 1940 2020-05-13 South West 7
## 1941 2020-05-14 South West 6
## 1942 2020-05-15 South West 4
## 1943 2020-05-16 South West 4
## 1944 2020-05-17 South West 6
## 1945 2020-05-18 South West 4
## 1946 2020-05-19 South West 6
## 1947 2020-05-20 South West 1
## 1948 2020-05-21 South West 9
## 1949 2020-05-22 South West 7
## 1950 2020-05-23 South West 6
## 1951 2020-05-24 South West 3
## 1952 2020-05-25 South West 8
## 1953 2020-05-26 South West 11
## 1954 2020-05-27 South West 5
## 1955 2020-05-28 South West 10
## 1956 2020-05-29 South West 7
## 1957 2020-05-30 South West 3
## 1958 2020-05-31 South West 2
## 1959 2020-06-01 South West 7
## 1960 2020-06-02 South West 2
## 1961 2020-06-03 South West 7
## 1962 2020-06-04 South West 2
## 1963 2020-06-05 South West 2
## 1964 2020-06-06 South West 1
## 1965 2020-06-07 South West 3
## 1966 2020-06-08 South West 3
## 1967 2020-06-09 South West 0
## 1968 2020-06-10 South West 1
## 1969 2020-06-11 South West 2
## 1970 2020-06-12 South West 2
## 1971 2020-06-13 South West 2
## 1972 2020-06-14 South West 0
## 1973 2020-06-15 South West 2
## 1974 2020-06-16 South West 2
## 1975 2020-06-17 South West 0
## 1976 2020-06-18 South West 0
## 1977 2020-06-19 South West 0
## 1978 2020-06-20 South West 2
## 1979 2020-06-21 South West 0
## 1980 2020-06-22 South West 1
## 1981 2020-06-23 South West 1
## 1982 2020-06-24 South West 1
## 1983 2020-06-25 South West 0
## 1984 2020-06-26 South West 3
## 1985 2020-06-27 South West 0
## 1986 2020-06-28 South West 0
## 1987 2020-06-29 South West 1
## 1988 2020-06-30 South West 0
## 1989 2020-07-01 South West 0
## 1990 2020-07-02 South West 0
## 1991 2020-07-03 South West 0
## 1992 2020-07-04 South West 0
## 1993 2020-07-05 South West 1
## 1994 2020-07-06 South West 0
## 1995 2020-07-07 South West 0
## 1996 2020-07-08 South West 2
## 1997 2020-07-09 South West 0
## 1998 2020-07-10 South West 1
## 1999 2020-07-11 South West 0
## 2000 2020-07-12 South West 0
## 2001 2020-07-13 South West 1
## 2002 2020-07-14 South West 0
## 2003 2020-07-15 South West 0
## 2004 2020-07-16 South West 0
## 2005 2020-07-17 South West 1
## 2006 2020-07-18 South West 0
## 2007 2020-07-19 South West 0
## 2008 2020-07-20 South West 0
## 2009 2020-07-21 South West 0
## 2010 2020-07-22 South West 0
## 2011 2020-07-23 South West 0
## 2012 2020-07-24 South West 0
## 2013 2020-07-25 South West 0
## 2014 2020-07-26 South West 0
## 2015 2020-07-27 South West 0
## 2016 2020-07-28 South West 0
## 2017 2020-07-29 South West 0
## 2018 2020-07-30 South West 1
## 2019 2020-07-31 South West 0
## 2020 2020-08-01 South West 0
## 2021 2020-08-02 South West 0
## 2022 2020-08-03 South West 0
## 2023 2020-08-04 South West 0
## 2024 2020-08-05 South West 0
## 2025 2020-08-06 South West 0
## 2026 2020-08-07 South West 0
## 2027 2020-08-08 South West 0
## 2028 2020-08-09 South West 0
## 2029 2020-08-10 South West 0
## 2030 2020-08-11 South West 0
## 2031 2020-08-12 South West 0
## 2032 2020-08-13 South West 0
## 2033 2020-08-14 South West 1
## 2034 2020-08-15 South West 0
## 2035 2020-08-16 South West 0
## 2036 2020-08-17 South West 2
## 2037 2020-08-18 South West 0
## 2038 2020-08-19 South West 0
## 2039 2020-08-20 South West 0
## 2040 2020-08-21 South West 0
## 2041 2020-08-22 South West 0
## 2042 2020-08-23 South West 0
## 2043 2020-08-24 South West 0
## 2044 2020-08-25 South West 1
## 2045 2020-08-26 South West 0
## 2046 2020-08-27 South West 1
## 2047 2020-08-28 South West 0
## 2048 2020-08-29 South West 0
## 2049 2020-08-30 South West 0
## 2050 2020-08-31 South West 0
## 2051 2020-09-01 South West 0
## 2052 2020-09-02 South West 0
## 2053 2020-09-03 South West 0
## 2054 2020-09-04 South West 0
## 2055 2020-09-05 South West 0
## 2056 2020-09-06 South West 0
## 2057 2020-09-07 South West 0
## 2058 2020-09-08 South West 1
## 2059 2020-09-09 South West 0
## 2060 2020-09-10 South West 0
## 2061 2020-09-11 South West 0
## 2062 2020-09-12 South West 0
## 2063 2020-09-13 South West 1
## 2064 2020-09-14 South West 0
## 2065 2020-09-15 South West 0
## 2066 2020-09-16 South West 0
## 2067 2020-09-17 South West 1
## 2068 2020-09-18 South West 0
## 2069 2020-09-19 South West 0
## 2070 2020-09-20 South West 1
## 2071 2020-09-21 South West 0
## 2072 2020-09-22 South West 0
## 2073 2020-09-23 South West 0
## 2074 2020-09-24 South West 1
## 2075 2020-09-25 South West 0
## 2076 2020-09-26 South West 0
## 2077 2020-09-27 South West 0
## 2078 2020-09-28 South West 0
## 2079 2020-09-29 South West 0
## 2080 2020-09-30 South West 0
## 2081 2020-10-01 South West 0
## 2082 2020-10-02 South West 1
## 2083 2020-10-03 South West 0
## 2084 2020-10-04 South West 0
## 2085 2020-10-05 South West 0
## 2086 2020-10-06 South West 1
## 2087 2020-10-07 South West 0
## 2088 2020-10-08 South West 1
## 2089 2020-10-09 South West 1
## 2090 2020-10-10 South West 0
## 2091 2020-10-11 South West 4
## 2092 2020-10-12 South West 2
## 2093 2020-10-13 South West 0
## 2094 2020-10-14 South West 3
## 2095 2020-10-15 South West 1
## 2096 2020-10-16 South West 2
## 2097 2020-10-17 South West 8
## 2098 2020-10-18 South West 2
## 2099 2020-10-19 South West 2
## 2100 2020-10-20 South West 3
## 2101 2020-10-21 South West 6
## 2102 2020-10-22 South West 6
## 2103 2020-10-23 South West 5
## 2104 2020-10-24 South West 5
## 2105 2020-10-25 South West 5
## 2106 2020-10-26 South West 7
## 2107 2020-10-27 South West 6
## 2108 2020-10-28 South West 8
## 2109 2020-10-29 South West 11
## 2110 2020-10-30 South West 8
## 2111 2020-10-31 South West 4
## 2112 2020-11-01 South West 5
## 2113 2020-11-02 South West 11
## 2114 2020-11-03 South West 7
## 2115 2020-11-04 South West 8
## 2116 2020-11-05 South West 5
## 2117 2020-11-06 South West 11
## 2118 2020-11-07 South West 10
## 2119 2020-11-08 South West 10
## 2120 2020-11-09 South West 12
## 2121 2020-11-10 South West 6
## 2122 2020-11-11 South West 13
## 2123 2020-11-12 South West 17
## 2124 2020-11-13 South West 9
## 2125 2020-11-14 South West 8
## 2126 2020-11-15 South West 16
## 2127 2020-11-16 South West 18
## 2128 2020-11-17 South West 17
## 2129 2020-11-18 South West 26
## 2130 2020-11-19 South West 15
## 2131 2020-11-20 South West 25
## 2132 2020-11-21 South West 25
## 2133 2020-11-22 South West 23
## 2134 2020-11-23 South West 14
## 2135 2020-11-24 South West 20
## 2136 2020-11-25 South West 25
## 2137 2020-11-26 South West 16
## 2138 2020-11-27 South West 21
## 2139 2020-11-28 South West 35
## 2140 2020-11-29 South West 15
## 2141 2020-11-30 South West 21
## 2142 2020-12-01 South West 18
## 2143 2020-12-02 South West 15
## 2144 2020-12-03 South West 14
## 2145 2020-12-04 South West 19
## 2146 2020-12-05 South West 17
## 2147 2020-12-06 South West 13
## 2148 2020-12-07 South West 14
## 2149 2020-12-08 South West 18
## 2150 2020-12-09 South West 21
## 2151 2020-12-10 South West 20
## 2152 2020-12-11 South West 19
## 2153 2020-12-12 South West 15
## 2154 2020-12-13 South West 19
## 2155 2020-12-14 South West 19
## 2156 2020-12-15 South West 17
## 2157 2020-12-16 South West 8
## 2158 2020-12-17 South West 24
## 2159 2020-12-18 South West 10
## 2160 2020-12-19 South West 20
## 2161 2020-12-20 South West 18
## 2162 2020-12-21 South West 20
## 2163 2020-12-22 South West 10
## 2164 2020-12-23 South West 16
## 2165 2020-12-24 South West 18
## 2166 2020-12-25 South West 19
## 2167 2020-12-26 South West 22
## 2168 2020-12-27 South West 21
## 2169 2020-12-28 South West 17
## 2170 2020-12-29 South West 18
## 2171 2020-12-30 South West 13
## 2172 2020-12-31 South West 23
## 2173 2021-01-01 South West 25
## 2174 2021-01-02 South West 16
## 2175 2021-01-03 South West 20
## 2176 2021-01-04 South West 23
## 2177 2021-01-05 South West 4We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Wednesday 06 Jan 2021.
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_reportWe plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lagThis analysis suggests that the best lag is 16 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 16 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -16.244 -11.319 -4.340 8.037 18.697
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.569e+00 6.558e-02 69.68 <2e-16 ***
## note_lag 1.652e-05 9.055e-07 18.24 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 99.41176)
##
## Null deviance: 50148 on 256 degrees of freedom
## Residual deviance: 26475 on 255 degrees of freedom
## (16 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 96.483451 1.000017
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 84.642479 109.463976
## note_lag 1.000015 1.000018
Rsq(lag_mod)
## [1] 0.4720584
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fitThis is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom")
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_regWe save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))The following information documents the system on which the document was compiled.
This provides information on the operating system.
Sys.info()
## sysname
## "Darwin"
## release
## "19.6.0"
## version
## "Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/RELEASE_X86_64"
## nodename
## "Mac-1610015021945.local"
## machine
## "x86_64"
## login
## "root"
## user
## "runner"
## effective_user
## "runner"This provides information on the version of R used:
This provides information on the packages used:
sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.4 ggpubr_0.4.0 lubridate_1.7.9.2
## [4] chngpt_2020.10-12 cyphr_1.1.0 DT_0.17
## [7] kableExtra_1.3.1 janitor_2.1.0 remotes_2.2.0
## [10] projections_0.5.2 earlyR_0.0.5 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.3 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.6 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
## [25] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.3
## [28] tidyverse_1.3.0 here_1.0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] minqa_1.2.4 colorspace_2.0-0 selectr_0.4-2 ggsignif_0.6.0
## [5] ellipsis_0.3.1 rprojroot_2.0.2 snakecase_0.11.0 fs_1.5.0
## [9] rstudioapi_0.13 farver_2.0.3 fansi_0.4.1 splines_4.0.3
## [13] knitr_1.30 jsonlite_1.7.2 nloptr_1.2.2.2 broom_0.7.3
## [17] dbplyr_2.0.0 compiler_4.0.3 httr_1.4.2 backports_1.2.1
## [21] assertthat_0.2.1 Matrix_1.2-18 cli_2.2.0 htmltools_0.5.0
## [25] tools_4.0.3 gtable_0.3.0 glue_1.4.2 Rcpp_1.0.5
## [29] carData_3.0-4 cellranger_1.1.0 vctrs_0.3.6 nlme_3.1-149
## [33] matchmaker_0.1.1 crosstalk_1.1.0.1 xfun_0.20 ps_1.5.0
## [37] openxlsx_4.2.3 lme4_1.1-26 lifecycle_0.2.0 statmod_1.4.35
## [41] rstatix_0.6.0 MASS_7.3-53 scales_1.1.1 hms_0.5.3
## [45] parallel_4.0.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [49] gridExtra_2.3 stringi_1.5.3 kyotil_2020.10-12 boot_1.3-25
## [53] zip_2.1.1 rlang_0.4.10 pkgconfig_2.0.3 evaluate_0.14
## [57] lattice_0.20-41 labeling_0.4.2 htmlwidgets_1.5.3 cowplot_1.1.1
## [61] tidyselect_1.1.0 plyr_1.8.6 magrittr_2.0.1 R6_2.5.0
## [65] generics_0.1.0 DBI_1.1.0 pillar_1.4.7 haven_2.3.1
## [69] foreign_0.8-80 withr_2.3.0 mgcv_1.8-33 survival_3.2-7
## [73] abind_1.4-5 modelr_0.1.8 crayon_1.3.4 car_3.0-10
## [77] utf8_1.1.4 rmarkdown_2.6 viridis_0.5.1 grid_4.0.3
## [81] readxl_1.3.1 data.table_1.13.6 reprex_0.3.0 digest_0.6.27
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0